Experimental Evaluation of Maximum-Likelihood-Based Data Preconditioning for DE-SPECT: A Clinical SPECT System Constructed with CZT Imaging Detectors

• Published in: 2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and International Symposium on Room-Temperature Semiconductor Detectors (NSS MIC RTSD) p. 1
• Main Authors: Jin, Y., Zannoni, E. M., Meng, L.J.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 04.11.2023
• Subjects: Detectors; Energy resolution; Image resolution; Maximum likelihood detection; Microwave integrated circuits; Semiconductor detectors; Semiconductor device measurement
• ISSN: 2577-0829
• DOI: 10.1109/NSSMICRTSD49126.2023.10337951

We present a maximum-likelihood (ML) -based data preconditioning method for a 3-D position sensitive CZT detector used in the DE-SPECT imaging system, a clinical SPECT system dedicated for imaging the peripheral vascular diseases (PVD) in lower extremities. The 3-D CZT detectors offer subpixel resolution of <0.5 mm FWHM in X-Y-Z directions and an ultrahigh energy resolution of 3 keV at 200 keV, 4.5 keV at 450 keV, and 5.4 keV at 511 keV. Due to pixel edge effects and distortion issues, we utilized sheet beam scanning to measure the detector response, and then used an ML-based algorithm to reconstruct the projection, which effectively deconvolve the distortions in detector linear responses. As we showed in the experimental results, this technique produces corrected positions of interactions. Additionally, we anticipate that this approach can be applied to other solid-state and scintillation detectors with known distortion due to imperfections in detectors and electronics. Future work includes phantom studies to evaluate the effect of the data preconditioning approach on experimental SPECT imaging studies.